System and method for acceleration-based vector field maps

ABSTRACT

In an autonomous vehicle system, data received from one or more autonomous vehicles (AVs) can be aggregated to generate aggregated data. From this aggregated data, a vector-field map can be generated that includes a plurality of cells. Each of the cells can include a corresponding vector. The vector field map can be analyzed to identify one or more vectors of the plurality of cells that exceed one or more predetermined threshold values. The analysis can include a magnitude analysis and/or a frequency analysis. Based on the analysis, traffic and/or road conditions can be determined, which can provide prior knowledge about the driving behavior of other vehicles. Advantageously, aspects of the disclosure improve predictive motion models and enhance navigation algorithms.

TECHNICAL FIELD

Aspects described herein generally relate to acceleration-based vectorfield map generation, more particularly, to techniques for generatingand analyzing acceleration-based vector field maps that may be used byself-driving systems.

BACKGROUND

Autonomous driving vehicles may be equipped with one or more sensorsthat are used for navigation, trajectory planning and state estimation.Data obtained from these sensors can be used by the respective vehicle,as well as provided to one or more other vehicles and/or remote systemsfor further processing. The data can be communicated via aVehicle-to-Everything (V2X) communication system that includes thepassing of information from a vehicle to any entity that may affect thevehicle, and vice versa. V2X is a vehicular communication system thatincorporates other more specific types of communication, such asVehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V),Vehicle-to-Device (V2D), Vehicle-to-Pedestrian (V2P), Vehicle-to-grid(V2G), etc. V2X can include cellular V2X (C-V2X), which includes themore specific types of communication above and can includevehicle-to-network (V2N) communications (e.g., via one or more cellulartechnologies). V2X is used as an example type of communication forpurposes of explanation, but the disclosure is not limited in thisrespect.

As the technology for self-driving systems advances, so do the safetyconcerns. Many real-life examples ranging from L1 (driving assistancesystems) to L5 (highly automated systems) of deployed and testingsystems have demonstrated that driving safety cannot rely solely onprobabilistic estimations nor on attentive drivers. This is why formalsafety driving models aim to close the safety gap that exists todaywithin self-driving systems. These models leverage data and otherinformation provided by the autonomous driving vehicles (AVs).

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate the aspects of the present disclosureand, together with the description, and further serve to explain theprinciples of the aspects and to enable a person skilled in thepertinent art to make and use the aspects.

FIG. 1 illustrates an acceleration vector field map in accordance withan aspect of the disclosure.

FIG. 2A illustrates a map generation system in accordance with an aspectof the disclosure.

FIG. 2B illustrates an autonomous vehicle in accordance with an aspectof the disclosure.

FIG. 3 illustrates a map generation system in accordance with an aspectof the disclosure.

FIG. 4 illustrates a map generation system in accordance with an aspectof the disclosure.

FIG. 5 illustrates a data aggregation and indexing process in accordancewith an aspect of the disclosure.

FIG. 6 illustrates an acceleration vector field map in accordance withan aspect of the disclosure.

FIG. 7A illustrates a portion of the acceleration vector field map ofFIG. 6 in accordance with an aspect of the disclosure.

FIG. 7B illustrates the portion of the acceleration vector field map ofFIG. 7A following a vector magnitude filtering in accordance with anaspect of the disclosure.

FIG. 8A illustrates a portion of the acceleration vector field map ofFIG. 6 in accordance with an aspect of the disclosure.

FIG. 8B illustrates the portion of the acceleration vector field map ofFIG. 8A following a high-pass frequency filtering in accordance with anaspect of the disclosure.

FIG. 8C illustrates the portion of the acceleration vector field map ofFIG. 8B following a noise removal filtering in accordance with an aspectof the disclosure.

FIG. 9 illustrates a flowchart of a robust probabilistic filteringmethod in accordance with an aspect of the disclosure.

FIG. 10 illustrates a flowchart of a map generation method in accordancewith an aspect of the disclosure.

The exemplary aspects of the present disclosure will be described withreference to the accompanying drawings. The drawing in which an elementfirst appears is typically indicated by the leftmost digit(s) in thecorresponding reference number.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the aspects of the presentdisclosure. However, it will be apparent to those skilled in the artthat the aspects, including structures, systems, and methods, may bepracticed without these specific details. The description andrepresentation herein are the common means used by those experienced orskilled in the art to most effectively convey the substance of theirwork to others skilled in the art. In other instances, well-knownmethods, procedures, components, and circuitry have not been describedin detail to avoid unnecessarily obscuring aspects of the disclosure.

Autonomous driving vehicles are equipped with a suite of sensors thatare used for navigation, trajectory planning and state estimation. Inone or more exemplary aspects, the autonomous driving vehicles (AVs) areequipped with one or more inertial measurement (IM) sensor, alsoreferred to as an inertial sensor. The IM sensor(s) can be used forGlobal Positioning System (GPS) localization and state estimation.

With a fleet of N autonomous vehicles, each vehicle implements its ownstate estimation capabilities through multiple means, including but notlimited to: GPS, IM sensors, Visual Odometry and simultaneouslocalization and mapping (SLAM). Advantageously, aspects of the presentdisclosure harness the data from the IM sensors for use beyond stateestimation that includes leveraging the acceleration values sensed by IMsensors to generate a spatio-temporal map layer of expectedaccelerations. Such a map may be used to compute analytics about thetraffic and road conditions, provide prior knowledge about the drivingbehavior of other vehicles to improve predictive motion models, andenhance navigation algorithms.

Aspects include a map generation system that is configured tocrowdsource information (e.g. acceleration information) to generateacceleration vector field maps, as well as to process the generatedvector field maps to detect salient points in the road to be furtheranalyzed by road and traffic maintenance officials. In other aspects,the acceleration field vector map is used to adapt navigation andprediction engines of autonomous vehicles.

Information obtained from the IM sensor(s) can be stored in a memory ofthe corresponding AV and/or can be communicated to one or other AVsand/or one or more remote systems. In an exemplary aspect, the IM sensorincludes accelerometer, a gyroscope and/or a magnetometer, and can beconfigured to determine information related to road conditions, drivingstyle, reaction times, and/or one or more other vehicle and/orenvironmental parameters as would be understood by one of ordinary skillin the art. In exemplary aspects, information from the IM sensors of oneor more vehicles, such as acceleration, is processed to generateacceleration-based vector field maps. Advantageously, with thegeneration of the acceleration-based vector field maps, aspects of thedisclosure provide extended context awareness for the AVs andcorresponding environment.

In an exemplary aspect, the system is configured to harvest time andspace geo-referenced IM sensor signals, through crowdsourcing, from oneor more AVs. The harvested information is processed to generate anacceleration-based map layer, which can be used for trajectoryprediction, road condition analysis, traffic enhancement, and/or one ormore other applications as would be understood by one of ordinary skillin the art. In an exemplary aspect, the autonomous vehicle systemutilizes the received crowd-sourced vector-field map for path planningand/or to adjust/influence results or operations of a path planningmodule.

In an exemplary aspect, with reference to FIG. 1 , the system isconfigured to aggregate sensor data to determine cell-wise statistics ofthe acceleration-based vector field map 100. In an exemplary aspect, thesystem is configured to determine the mean, mode, and/or one or moreother statistical calculations on the aggregated data to generate thevector field map 100 that contains the expected acceleration per cell.The acceleration-based vector field map 100 includes a grid 110 of cells105 corresponding to the road or other navigational location. Each cell105 including an acceleration vector 120 that is determined based ondata provided by one or more autonomous vehicles (AVs) 202.1 to 202.n(FIG. 2A).

As illustrated in FIG. 2A, the map generation system (MGS) 205 isconfigured to communicate with one or more autonomous vehicles 202.1 to202.n to generate the acceleration-based vector field map 100. Thecommunication can use one or more wireless communication protocols, butis not limited thereto. The AVs 202 are further described with referenceto FIG. 2B.

In an exemplary aspect, the MGS 205 includes controller 210, memory 220,and a communication interface 225. The memory 220 can store data and/orinstructions, where when the instructions are executed by the controlled210, controls the controller 210 to perform the functions describedherein. The memory 220 may be any well-known volatile and/ornon-volatile memory, including, for example, read-only memory (ROM),random access memory (RAM), flash memory, a magnetic storage media, anoptical disc, erasable programmable read only memory (EPROM), andprogrammable read only memory (PROM). The memory 220 can benon-removable or removable, or a combination of both.

In an exemplary aspect, the communication interface 225 is configured totransmit and/or receive wireless communications via one or more wirelesstechnologies. In an exemplary aspect, the communication interface 225 isincludes processor circuitry that is configured for transmitting and/orreceiving wireless communications conforming to one or more wirelessprotocols.

The one or more wireless protocols may include one or more fifthgeneration (5G) cellular communication protocols, such as 5G protocols,one or more 4^(th) Generation cellular communication protocols, one ormore 3rd Generation Partnership Project's (3GPP) protocols (e.g.,Long-Term Evolution (LTE)), one or more wireless local area networking(WLAN) communication protocols, and/or one or more other communicationprotocols (e.g. Bluetooth, millimeter wave (mmWave), microwave).

In an exemplary aspect, the communication interface 225 includes atransmitter and a receiver (transceiver) configured for transmitting andreceiving wireless communications, respectively, via one or moreantennas, access points, and/or base stations 230. In aspects having twoor more transceivers, the two or more transceivers can have their ownantenna, or can share a common antenna via a duplexer. In an exemplaryaspect, the communication interface 225 is configured to perform one ormore baseband processing functions (e.g., media access control (MAC),encoding/decoding, modulation/demodulation, data symbol mapping; errorcorrection, etc.). The antenna 230 can include one or more antennaelements forming an integer array of antenna elements. In an exemplaryaspect, the antenna 230 is a phased array antenna that includes multipleradiating elements (antenna elements) each having a corresponding phaseshifter. The antenna 230 configured as a phased array antenna can beconfigured to perform one or more beamforming operations that includegenerating beams formed by shifting the phase of the signal emitted fromeach radiating element to provide constructive/destructive interferenceso as to steer the beams in the desired direction.

In an exemplary aspect, the controller 210 includes processor circuitythat is configured to control the overall operation of the MGS 205, suchas the operation of the communication interface 225. The controller 210may be configured to control the transmitting and/or receiving ofwireless communications via the communication interface 225.

In an exemplary aspect, the controller 210 is configured to perform oneor more baseband processing functions (e.g., media access control (MAC),encoding/decoding, modulation/demodulation, data symbol mapping; errorcorrection, etc.) in cooperation with the communication interface 225 orinstead of such operations/functions being performed by thecommunication interface 225. The controller 210 is configured to run oneor more applications and/or operating systems; power management (e.g.,battery control and monitoring); display settings; volume control;and/or user interactions via one or more user interfaces (e.g.,keyboard, touchscreen display, microphone, speaker, etc.) in one or moreaspects.

In an exemplary aspect, the MGS 205 is configured to receiveacceleration data from the AVs 202 using the communication interface225. The received acceleration data can be stored in memory 220. In anexemplary aspect, the acceleration data is geo-localized by one or morestate estimation algorithms of the AV 202. That is, the accelerationdata is associated with a corresponding location (e.g. via GPS) andtime. The MGS 205 can than then store the acceleration information in acorresponding grid of the vector field map according to itscorresponding spatio-temporal coordinates x(ϕ, λ, t) (latitude,longitude, time) provided by the AV 202.

The controller 210 is configured to perform one or more processingoperations (e.g. aggregation, spatio-temporal vector field generation,frequency analysis, magnitude analysis) on the acceleration data togenerate one or more acceleration-based vector field maps 100. Theacceleration-based vector field map 100 and/or information derivedtherefrom can be stored in memory 220 and/or communicated back to one ormore of the AVs 202.

In an exemplary aspect, the controller 210 may utilize one or moremachine learning models to perform functions of the controller 210, oneor more other components of the MGS 205, and/or to control the overalloperation of the MGS 205.

In an exemplary aspect, the MGS 205 is configured to perform frequencyanalysis and/or magnitude analysis on the acceleration-based vectorfield map 100 to provide road condition information and/or trafficinformation. For example, road condition information can be used todetect road conditions and/or road hazards. In an exemplary aspect, theMGS 205 perform frequency analysis (e.g. in the frequency domain) on thevector field map to identify which of the cells 115 (e.g. P cells—FIGS.3-4 ) include high-frequency components normal to the ground plane. Highfrequency components may indicate the presence of potholes, bumps orother defects in the road.

In an exemplary aspect, the MGS 205 is configured to perform an analysisof the magnitudes of the vector field map to identify trafficconditions. In an exemplary aspect, the MGS 205 is configured to analyzethe sum of magnitudes of vectors during traffic light transitions toadapt traffic control configurations to reduce the overall accelerationsin the vicinity.

FIG. 3 illustrates an exemplary aspect of the MGS 205. In this aspect,the controller 210 includes a data aggregator 305, a spatio-temporalvector field generator 310, a frequency analyzer 315, and a magnitudeanalyzer 320. The MGS 205 is configured to receive sensor data from thereceived from one or more sensors (e.g. sensors 260—FIG. 2B) of the oneor more corresponding AVs 202, using the communication interface 225(shown in FIG. 2A).

The aggregator 305 is configured to aggregate sensor data (ϕ, λ, t,{right arrow over (a)})₁ to (ϕ, λ, t, {right arrow over (a)})_(n)received from one or more sensors (e.g. sensors 260—FIG. 2B) of the oneor more corresponding AVs 202. In an exemplary aspect, the sensors(sensors 260) include inertial measurement (IM) sensors, where thesensor data that is aggregated by the aggregator 205 is accelerationdata of the corresponding AV 202.

The aggregated acceleration data ({right arrow over (â)})_(ϕ, λ, t) isprovided to the spatio-temporal vector field generator 310, which isconfigured to process the aggregated acceleration data (that may beindexed by latitude, longitude and time) to generate a singleacceleration vector 120 for each cell 115 of the vector field map 110.The acceleration vectors 120 form the map represented by ({right arrowover (â)})_(ϕ, λ, t) in FIG. 1 . In this example, the aggregatedacceleration information can be represented as a set of accelerationvalues: D (ϕ, λ, t)→{right arrow over (a)}_(0:n). To generate the singleacceleration vector 120, the spatio-temporal vector field generator 310can perform one or more filtering processing operations, including meanfiltering (e.g. compute the average acceleration value from all thereadings) and/or robust probabilistic filtering (e.g. a probabilisticoutlier rejection method that computes the expected acceleration valueby computing the mean after rejecting the outliers).

The vector field map 110 is then provided to the frequency analyzer 315and the magnitude analyzer 320. The magnitude analyzer 320 can filterthe magnitude of the acceleration vectors of the corresponding cells todetermine cells/locations where the magnitude of the accelerationexceeds a threshold value, which may be indicative of road and/ortraffic conditions 150. The magnitude analyzer 320 can generate anoutput signal (ϕ, λ, t)_(Q) corresponding to the analyzed magnitude forthe respective cell. Here, the variables (ϕ, λ, t) correspond to thelatitude, longitude, time, respectively of the corresponding cell.

The frequency analyzer 315 can be configured to perform one or morefrequency processing operations, such as a Clifford Fouriertransformation. In this example, a high-pass filter may be applied tothe Clifford Fourier transform to determine cells/locations that includehigh frequency components of the vector field. In an exemplary aspect,high frequency components correspond to cell where the accelerationvalues are fast changing (area 150 in FIG. 1 ). These fast changingacceleration values may indicate possibly anomalies in the vector field,which may be caused by, for example, poor road conditions. The frequencyanalyzer 315 can generate an output signal (ϕ, λ, t)_(P) correspondingto the analyzed frequencies for the respective cell.

The output signal of the frequency analyzer 315 and/or the output signalof the magnitude analyzer 320 can be used by the MGS 205 to, forexample, determine analytics about the traffic and road conditions,identify driving behavior of one or more AVs to improve predictivemotion models and/or enhance navigation algorithms. The output signal ofthe frequency analyzer 315 and/or the output signal of the magnitudeanalyzer 320 can additionally or alternatively be provided (e.g.wirelessly communicated) to one or more AVs 202 by the MGS 205. As shownin FIG. 2B, which illustrates an AV 202 according to an exemplaryaspect, the AVs 202 are configured to communicate with the MGS 205,which can provide the generated acceleration-based vector field map 100,the output signal of the frequency analyzer 315, and/or the outputsignal of the magnitude analyzer 320, to one or more of the AVs 202. Thecommunication can use one or more wireless communication protocols, butis not limited thereto.

In an exemplary aspect, the AV 202 includes controller 250, memory 255,one or more sensors 260, and a communication interface 265. The memory255 can store data and/or instructions, where when the instructions areexecuted by the controlled 250, controls the controller 250 to performthe functions described herein. The memory 255 may be any well-knownvolatile and/or non-volatile memory, including, for example, read-onlymemory (ROM), random access memory (RAM), flash memory, a magneticstorage media, an optical disc, erasable programmable read only memory(EPROM), and programmable read only memory (PROM). The memory 255 can benon-removable or removable, or a combination of both. The memory 255 canalso store an acceleration-based vector field map 100, frequencyanalysis information, and/or magnitude analysis information.

In an exemplary aspect, the communication interface 265 is configured totransmit and/or receive wireless communications via one or more wirelesstechnologies. In an exemplary aspect, the communication interface 265 isincludes processor circuitry that is configured for transmitting and/orreceiving wireless communications conforming to one or more wirelessprotocols.

The one or more wireless protocols may include one or more fifthgeneration (5G) cellular communication protocols, such as 5G protocols,one or more 4^(th) Generation cellular communication protocols, one ormore 3rd Generation Partnership Project's (3GPP) protocols (e.g.,Long-Term Evolution (LTE)), one or more wireless local area networking(WLAN) communication protocols, and/or one or more other communicationprotocols (e.g. Bluetooth, millimeter wave (mmWave), microwave).

In an exemplary aspect, the communication interface 265 includes atransmitter and a receiver (transceiver) configured for transmitting andreceiving wireless communications, respectively. In an exemplary aspect,the communication interface 265 is configured to perform one or morebaseband processing functions (e.g., media access control (MAC),encoding/decoding, modulation/demodulation, data symbol mapping; errorcorrection, etc.).

In an exemplary aspect, the controller 250 includes processor circuitythat is configured to control the overall operation of the AV 202, suchas the operation of the communication interface 265 and/or sensor 260.The controller 250 may be configured to control the transmitting and/orreceiving of wireless communications via the communication interface265, and/or processing of sensor information obtained by the sensor 260.

In an exemplary aspect, the controller 250 is configured to perform oneor more baseband processing functions (e.g., media access control (MAC),encoding/decoding, modulation/demodulation, data symbol mapping; errorcorrection, etc.) in cooperation with the communication interface 265 orinstead of such operations/functions being performed by thecommunication interface 265. The controller 250 is configured to run oneor more applications and/or operating systems; power management (e.g.,battery control and monitoring); display settings; volume control;and/or user interactions via one or more user interfaces (e.g.,keyboard, touchscreen display, microphone, speaker, etc.) in one or moreaspects.

In an exemplary aspect, the controller 250 is configured to utilize theacceleration-based vector field map 100, frequency analysis information,magnitude analysis information, and/or other information derived from orrelated to the acceleration-based vector field map 100, frequencyanalysis information, and/or magnitude analysis information to, forexample, determine analytics about the traffic and road conditions, andidentify driving behavior of one or more AVs to improve predictivemotion models and/or enhance navigation algorithms. In an exemplaryaspect, the controller 250 may utilize one or more machine learningmodels to perform functions of the controller 250, one or more othercomponents of the AV 202, and/or to control the overall operation of theAV 202.

The one or more sensors 260 can include one or more inertial measurement(IM) sensors, one or more Global Positioning System (GPS) sensors,and/or more other sensors as would be understood by one of ordinaryskill in the art. The IM sensors can include one or more accelerometers,gyroscopes, magnetometers, and/or more other sensors as would beunderstood by one of ordinary skill in the art.

FIG. 4 illustrates an exemplary aspect of the MGS 205. In this aspect,as in the controller 210 described with reference to FIG. 3 , thecontroller 210 includes data aggregator 305, spatio-temporal vectorfield generator 310, frequency analyzer 315, and magnitude analyzer 320.In this exemplary aspect, the controller 210 also includes astandardization processor 405, which will be described in more detailbelow. Again, the MGS 205 is configured to receive sensor data from thereceived from one or more sensors (e.g. sensors 260—FIG. 2B) of the oneor more corresponding AVs 202, using the communication interface 225(shown in FIG. 2A).

In an exemplary aspect, the MGS 205 is configured to discretizetraversable space to a configurable granularity δ_(x) (FIG. 1 ) anddiscretizes time with another granularity δ_(t). Here, the granularityδ_(x) corresponds to the cell size for the grid. The granularity δ_(x)can be for example, 10 cm, 1 m, or another dimension as would beunderstood by one of ordinary skill in the art. The acceleration datafrom the IM sensors 260 (e.g. acceleration {right arrow over (a)}),which can be geo-localized, are standardized and stored in the specificcell corresponding to the spatio-temporal coordinates x(ϕ, λ, t)(latitude, longitude, time) provided by the AV 202.

The MGS 205 can be configured to crowd source this information from theAVs 202 using one or more crowdsourcing methods to createspatio-temporal acceleration profiles that can be employed to supportone or more driving tasks (e.g. trajectory prediction, road conditionanalysis and traffic enhancement). The functions and operations of theMGS 205 as shown in FIG. 4 is provided below.

Standardization

In the example shown in FIG. 4 , there is a correspondingstandardization processor 405.1 to 405.n for each AV 202.1 to 202.n.However, the controller 210 can include a single standardizationprocessor 405 for all AVs 202, or can include a set of standardizationprocessors 405 that are less than the number of AVs 202, where some ofthe AVs 202 share a common standardization processor 405. In anexemplary aspect, the standardization processors 405, 410 are configuredto receive time-series geo-localized data frames from one or more AVs202. In an exemplary aspect, a data frame is defined as a tuple/orderedlist (a∈

³, ϕ∈

, λ∈

, t∈

. Here, a denotes the acceleration measured at time t in the vehicle nreference frame, that is localized at latitude ϕ and longitude λ. Forsimplicity, only ϕ and λ are used to describe the spatial coordinates.Multiple-layer roads and intersections can be taken into account byadding an extra altitude h∈

coordinate to the state tuple.

In an exemplary aspect, the data frames from the AVs 202 are split intotheir corresponding acceleration (a_(t))_(n) and the state estimationinformation (ϕ, λ, t)_(n) components. In this example, thestandardization processor 405 receives the acceleration (a_(t))_(n),which can be indexed by the AV 202 and the time. The standardizationprocessor 405 is configured to correct acceleration readings (a_(t))_(n)to account for each vehicle's specific transfer function that may impactthe acceleration signal (sensor model, shocks, tire pressure, load,etc.) into a standardized acceleration value (ā_(t))_(n).

The standardized acceleration value (ā_(t))_(n) is then combined withits corresponding state estimation information (ϕ, λ, t)_(n) to generatecombined data (ϕ, λ, t, ā)_(n). The combined data is then provided tothe data aggregator 305. The controller 210 can include an adder 410that is configured to combine the various inputs together to generatethe combined data (ϕ, λ, t, ā)_(n).

In an exemplary aspect, the standardization processor 405 is configuredto dynamically compute a standardization linear transformation T_(n)∈

^(3×3) for each AV 202.n. In an exemplary aspect, the standardizationprocessor 405 uses a linear transform to compute the standardizedacceleration value (a_(t))_(n)T_(n)=(ā_(t))_(n). In this example, T_(n)is an anisotropic scaling operation to the acceleration vector thattransforms its magnitude dimension-wise:

$T_{n} = {\begin{bmatrix}\tau_{x} & 0 & 0 \\0 & \tau_{y} & 0 \\0 & 0 & \tau_{z}\end{bmatrix} = \begin{bmatrix}\frac{1}{\frac{1}{w}{\sum\limits_{i = {t - w}}^{t}\left( {a_{x}} \right)_{i}}} & 0 & 0 \\0 & \frac{1}{\frac{1}{w}{\sum\limits_{i = {t - w}}^{t}\left( {a_{y}} \right)_{i}}} & 0 \\0 & 0 & \frac{1}{\frac{1}{w}{\sum\limits_{i = {t - w}}^{t}\left( {a_{z}} \right)_{i}}}\end{bmatrix}}$where the scale coefficients τ are computed as the inverse of theaverage observed absolute value with a rolling window of size w.

In an exemplary aspect, the standardization process may also usehistorical information about weather or other environmental factors,make and model of the AV, make and model of the sensor, tire pressure,load, and/or other factors that would be understood by one of ordinaryskill in the art.

Data Aggregation

The data aggregator 305 is configured to perform one or more dataaggregation processes to index the different data frames to provide amapping from spatio-temporal coordinates to the set of recordedacceleration values: D(ϕ, λ, t)→{right arrow over (a)}_(0:n). Theaggregator 305 is configured to aggregate the combined data (ϕ, λ, t,ā)_(n) from AVs 202 to generate aggregated acceleration data ({rightarrow over (â)})_(ϕ, λ, t) that is provided to the spatio-temporalvector field generator 310.

In an exemplary aspect, with reference to FIG. 5 , the data aggregator305 is configured to index the acceleration information using agrid-hierarchy indexer that groups neighboring cells together for fasterlocal access. For example, as shown in FIG. 5 , grid cell groups A, B,C, D, and E have been identified, where the data aggregator 305 canindex cells within each of these groups together for increased localaccess. The present disclosure is not limited to a grid-hierarchy indexscheme and other schemes are possible as would be understood by one ofordinary skill in the art.

Vector Field Generation

In an exemplary aspect, the spatio-temporal vector field generator 310is configured to process the aggregated acceleration data ({right arrowover (â)})_(ϕ, λ, t) (which is indexed by latitude, longitude and time)to generate a single acceleration vector 120 for each cell 115 of thevector field map 110. The acceleration vectors 120 form the maprepresented by ({right arrow over (â)})_(ϕ, λ, t) in FIG. 1 . Togenerate the single acceleration vector 120, the spatio-temporal vectorfield generator 310 can perform one or more filtering processingoperations, including mean filtering (e.g. compute the averageacceleration value from all the readings) and/or robust probabilisticfiltering (e.g. a probabilistic outlier rejection method that computesthe expected acceleration value by computing the mean after rejectingthe outliers).

In an exemplary aspect, the spatio-temporal vector field generator 310calculates an acceleration vector field V based on a selected summarystatistic method for all the cells 115 in the subspace of interest

∈

³. V=D(ϕ_(s) ₀ , λ_(s) ₁ , t_(s) ₂ ); ∀s∈

and V(ϕ, λ, t)→{right arrow over (â)}.

In an exemplary aspect, the spatio-temporal vector field generator 310is configured to, for each cell 115, summarize a corresponding set ofacceleration values in a single acceleration vector. In this example,for each cell 115, a set of acceleration values is indexed by latitude,longitude and time: D(ϕ, λ, t)→{right arrow over (a)}_(0:n). In anexemplary aspect, the spatio-temporal vector field generator 310 isconfigured to compute the vector field based on mean filtering and/orrobust probabilistic filter. For mean filtering, the spatio-temporalvector field generator 310 is configured to compute the averageacceleration value from all the sensor readings within the correspondingcell 115. Mean filtering provides a fast computation, but may beinfluenced by one or more outliers (e.g. generated by faulty sensors).For robust probabilistic filtering, a probabilistic outlier rejectionmethod computes the expected acceleration value by computing the meanafter rejecting the outliers. In an exemplary aspect, thespatio-temporal vector field generator 310 is configured to perform themean filtering as a faster, initial filtering. The map can be updatedbased on the more accurate robust probabilistic filtering.

The robust probabilistic filtering process according to an exemplaryaspect is illustrated in the flowchart shown in FIG. 9 . The filteringprocess includes determining the mean of a set of acceleration values(μ=mean(a)) at operation 910. At operation 915, the covariance of themean values is determined (Σ=cov(a)). At operation 920, outlieracceleration values are removed from the set of acceleration values.Here, an acceleration value is determined to be an outlier if the valueis three standard deviations (3σ) from the mean. At operation 925, it isdetermined if any outliers were removed from the set of accelerationvalues. If no outliers were removed, the filtered acceleration value isidentified at operation 935, and the process ends. If one or moreoutliers are removed from the set of acceleration values, the flowchartreturns to operation 910, where the flowchart is repeated until nooutliers are removed. In an exemplary aspect, the robust probabilisticfiltering can include fitting a multivariate Gaussian to the availabledata prior to the calculation of the mean and covariance.

An example acceleration vector field map 600 that is generated by thespatio-temporal vector field generator 310 based on the filteredacceleration values is shown in FIG. 6 . In FIG. 6 , the discretizationstep δ_(x) is 4 m (but is not limited thereto) and the accelerationvectors are projected onto a two-dimensional (2D) plane.

Magnitude and Frequency Analysis

The vector field map 600 is then provided to the frequency analyzer 315and the magnitude analyzer 320.

The magnitude analyzer 320 can filter the magnitude of the accelerationvectors of the corresponding cells to determine cells/locations wherethe magnitude of the acceleration exceeds a threshold value, which maybe indicative of road and/or traffic conditions.

A portion 700 of the vector field map 600 is shown FIG. 7A. Following amagnitude filtering, low magnitude acceleration values are filtered out,and the resulting vector field map 701 is shown in FIG. 7B. With thehigh magnitude acceleration values identified, the magnitude analyzer320 can generate an output signal (ϕ, λ, t)_(Q) corresponding to theanalyzed magnitude values with magnitudes that exceed the thresholdvalue, and may be indicative of indicative of possible road and/ortraffic conditions.

The frequency analyzer 315 can be configured to perform one or morefrequency processing operations, such as a Clifford Fouriertransformation, on the acceleration data as shown in FIGS. 8A-8C. FIG.8A shows a portion 800 of the vector field map 600. FIG. 8B showsfiltered acceleration values 801 following a high-pass frequencyfiltering. FIG. 8C shows filtered acceleration values 802 that have beenfiltered using a robust filtering. In this example, a high-pass filtermay be applied to the Clifford Fourier transform to determinecells/locations that include high frequency components of the vectorfield.

FIG. 8B illustrates that the high-pass frequency filtering can besensitive to noise, especially when the acceleration is low and the SNR(signal-to-noise ratio) is low. The robust frequency filtering caninclude the high-pass frequency filtering followed by an exclusion ofnoise based on the magnitude. FIG. 8C shows the cells that have beenidentified with the noise removed by discarding those vectors havingmagnitudes less than a noise threshold value.

In an exemplary aspect, high frequency components correspond to cellwhere the acceleration values are fast changing. These fast changingacceleration values may indicate possibly anomalies in the vector field,which may be caused by, for example, poor road conditions. The frequencyanalyzer 315 can generate an output signal (ϕ, λ, t)_(P) correspondingto the analyzed frequencies for the respective cell.

The output signal of the frequency analyzer 315 and/or the output signalof the magnitude analyzer 320 can be used by the MGS 205 to, forexample, determine analytics about the traffic and road conditions,identify driving behavior of one or more AVs to improve predictivemotion models and/or enhance navigation algorithms. The output signal ofthe frequency analyzer 315 and/or the output signal of the magnitudeanalyzer 320 can additionally or alternatively be provided (e.g.wirelessly communicated) to one or more AVs 202 by the MGS 205

FIG. 10 illustrates a map generation method 1000 according to anexemplary aspect of the present disclosure. The flowchart 1000 isdescribed with continued reference to FIGS. 1-9 . The operations of themethod are not limited to the order described below, and the variousoperations may be performed in a different order. Further, two or moreoperations of the methods may be performed simultaneously with eachother.

The flowchart 1000 begins with operations 1005, where accelerationinformation and state estimation information is received from one ormore AVs 202. In an exemplary aspect, the MGS 205 is configured toreceive sensor data and state estimation information from the AVs 202,where the information can be obtained by the corresponding AVs 202 fromone or more sensors (e.g. sensors 260—FIG. 2B). The sensor can be an IMsensor that is configured to detect an acceleration of the AV 202. Theacceleration information and state estimation information can becrowdsourced by the AVs

After operation 1005, the flowchart 1000 transitions to operation 1010,where the acceleration information and state estimation information areseparated. This operation can be performed for each set of accelerationand state estimation information received from a corresponding AV 202.

After operation 1010, the flowchart 1000 transitions to operation 1015,where the separated acceleration information is standardized. In anexemplary aspect, the standardization processor 405 corrects theacceleration information (a_(t))_(n) to account for vehicle-specifictransfer functions that may impact the acceleration signal (sensormodel, shocks, tire pressure, load, etc.). The standardization processgenerates a standardized acceleration value (ā_(t))_(n).

After operation 1015, the flowchart 1000 transitions to operation 1020,each of the standardized accelerations are recombined with theircorresponding state estimation information to generate combinedinformation/data.

After operation 1020, the flowchart 1000 transitions to operation 1025,where the combined information for each AV is aggregated to generateaggregated and indexed acceleration data. In an exemplary aspect, thedata aggregator 305 is configured to perform one or more dataaggregation processes to index the different data frames to provide amapping from spatio-temporal coordinates to the set of recordedacceleration values. The data aggregator 305 aggregates the combineddata (ϕ, λ, t, ā)_(n) from AVs 202 to generate aggregated accelerationdata ({right arrow over (â)})_(ϕ, λ, t) that is provided to thespatio-temporal vector field generator 310.

After operation 1025, the flowchart 1000 transitions to operation 1030,where the acceleration vector field map is generated based on aggregatedand indexed acceleration data. In an exemplary aspect, thespatio-temporal vector field generator 310 is configured to process theaggregated acceleration data ({right arrow over (â)})_(ϕ, λ, t) (whichis indexed by latitude, longitude and time) to generate a singleacceleration vector 120 for each cell 115 of the vector field map 110.

After operation 1030, the flowchart 1000 transitions to operation 1035,where magnitude and/or frequency analysis and filtering is performed onacceleration vector field map to identify high magnitude and/orfrequency cells within the map. In an exemplary aspect, the magnitudeanalyzer 320 can filter the magnitude of the acceleration vectors of thecorresponding cells to determine cells/locations where the magnitude ofthe acceleration exceeds a threshold value, which may be indicative ofroad and/or traffic conditions. The frequency analyzer 315 can performone or more frequency processing operations, such as a Clifford Fouriertransformation, on the acceleration data to determine cells/locationsthat include high frequency components of the vector field, which may beindicative of road and/or traffic conditions. The analyzed map can beused by the MGS 205 to compute analytics about the traffic and roadconditions, provide prior knowledge about the driving behavior of othervehicles to improve predictive motion models, and enhance navigationalgorithms. The information obtained from the map analysis can beprovided to one or more AVs 202 and/or one or more other systems ordevices within the autonomous driving environment.

EXAMPLES

The following examples pertain to further aspects.

Example 1 is a map generation system, comprising: a data aggregatorconfigured to aggregate data received from one or more autonomousvehicles (AVs) to generate aggregated data; a vector-field generatorconfigured to generate a vector-field map including a plurality of cellsbased on the aggregated data, each cell having a corresponding vector;and analyzer configured to: analyze the vector-field map to identify oneor more vectors of the plurality of cells exceeding one or morepredetermined threshold values; and generate an analyzed signalcorresponding to the identified one or more vectors and provide theanalyzed signal to the one or more AVs.

Example 2 is the subject matter of Example 1, wherein the data receivedfrom the one or more AVs is respective acceleration data of the one ormore AVs.

Example 3 is the subject matter of any of Examples 1-2, wherein theacceleration data of the one or more AVs includes corresponding timedata and location data.

Example 4 is the subject matter of any of Examples 1-3, wherein the datareceived from the one or more AVs is acceleration data detected by aninertial measurement sensor of the one or more AVs.

Example 5 is the subject matter of any of Examples 1-4, wherein the datareceived from the one or more AVs comprises state estimation informationand acceleration information.

Example 6 is the subject matter of any of Examples 1-5, furthercomprising a standardization processor that is configured to standardizethe acceleration information to generate standardized accelerationinformation, wherein the data aggregator is configured to aggregatestate estimation information and the standardized accelerationinformation to generate the aggregated data.

Example 7 is the subject matter of Example 6, wherein thestandardization processor is configured to perform a linear transform onthe acceleration information to generate the standardized accelerationinformation.

Example 8 is the subject matter of any of Examples 1-7, wherein thevector-field generator is configured to perform a mean filtering and/ora probabilistic filtering on the aggregated data to generate thevector-field map.

Example 9 is the subject matter of any of Examples 1-8, wherein the oneor more predetermined threshold values comprise a magnitude thresholdvalue and/or a frequency threshold value.

Example 10 is the subject matter of any of Examples 1-9, wherein theanalyzer is configured to analyze a magnitude and/or a frequency ofvectors of the vector-field map to identify the one or more vectors thatexceeding a predetermined magnitude threshold value and/or frequencythreshold value.

Example 11 is a non-transitory computer-readable storage medium with anexecutable computer program stored thereon, the program instructing aprocessor to: aggregate data received from one or more autonomousvehicles (AVs) to generate aggregated data; generate a vector-field mapincluding a plurality of cells based on the aggregated data, each cellhaving a corresponding vector; analyze the vector-field map to identifyone or more vectors of the plurality of cells exceeding one or morepredetermined threshold values; and generate an analyzed signalcorresponding to the identified one or more vectors and provide theanalyzed signal in electronic form as a data file.

Example 12 is the subject matter of Example 11, wherein the datareceived from the one or more AVs is respective acceleration data of theone or more AVs.

Example 13 is the subject matter of Example 12, wherein the accelerationdata of the one or more AVs includes corresponding time data andlocation data.

Example 14 is the subject matter of any of Examples 11-13, wherein thedata received from the one or more AVs is acceleration data detected byan inertial measurement sensor of the one or more AVs.

Example 15 is the subject matter of any of Examples 11-14, wherein thedata received from the one or more AVs comprises state estimationinformation and acceleration information.

Example 16 is the subject matter of Examples 15, wherein the programfurther instructs the processor to standardize the accelerationinformation to generate standardized acceleration information, whereinthe state estimation information and the standardized accelerationinformation are aggregated to generate the aggregated data.

Example 17 is the subject matter of Example 16, wherein standardizingthe acceleration information includes performing a linear transform onthe acceleration information to generate the standardized accelerationinformation.

Example 18 is the subject matter of any of Examples 11-17, whereingenerating the vector-field map includes performing a mean filteringand/or a probabilistic filtering on the aggregated data to generate thevector-field map.

Example 19 is the subject matter of any of Examples 11-18, wherein theone or more predetermined threshold values comprise a magnitudethreshold value and/or a frequency threshold value.

Example 20 is the subject matter of any of Examples 11-19, wherein theanalyzing the vector-field map comprises analyzing a magnitude and/or afrequency of vectors of the vector-field map to identify the one or morevectors that exceeding a predetermined magnitude threshold value and/orfrequency threshold value.

Example 21 is an autonomous vehicle (AV), comprising: an inertialmeasurement sensor configured to detect acceleration data of the AV; acommunication interface that is configured to: transmit the detectedacceleration data to a map generation system; and receive, from the mapgeneration system, a vector-field map that includes including aplurality of cells or vector-field map information determined from thevector-field map, the vector-field map being generated based on thedetected acceleration data, wherein each cell has a correspondingvector; and a controller configured to control the AV based on thevector-field map or the information determined from the vector-fieldmap.

Example 22 is the subject matter of Example 21, wherein the accelerationdata includes corresponding time data and location data.

Example 23 is the subject matter of any of Examples 21-22 wherein thecommunication interface is further configured to transmit stateestimation information of the AV to the map generation system, whereinthe vector-field map or the vector-field map information is generatedbased on the acceleration data and the state estimation information.

Example 24 is an autonomous vehicle system, comprising: one or moreautonomous vehicles (AVs); and a map generation system that includes: adata aggregator configured to aggregate data received from the one ormore AVs to generate aggregated data; a vector-field generatorconfigured to generate a vector-field map including a plurality of cellsbased on the aggregated data, each cell having a corresponding vector;and analyzer configured to: analyze the vector-field map to identify oneor more vectors of the plurality of cells exceeding one or morepredetermined threshold values; and generate an analyzed signalcorresponding to the identified one or more vectors and provide theanalyzed signal to the one or more AVs.

Example 25 is the subject matter of Example 24, wherein the one or moreAVs each include an inertial measurement sensor configured to detectacceleration data, wherein the data received from the one or more AVs isthe acceleration data detected by the inertial measurement sensor.

Example 26 is a map generation method, comprising: aggregating datareceived from one or more autonomous vehicles (AVs) to generateaggregated data; generating a vector-field map including a plurality ofcells based on the aggregated data, each cell having a correspondingvector; analyzing the vector-field map to identify one or more vectorsof the plurality of cells exceeding one or more predetermined thresholdvalues; and generating an analyzed signal corresponding to theidentified one or more vectors and providing the analyzed signal inelectronic form as a data file (e.g. to the AVs).

Example 27 is the subject matter of Example 26, wherein the datareceived from the one or more AVs is respective acceleration data of theone or more AVs.

Example 28 is the subject matter of Example 27, wherein the accelerationdata of the one or more AVs includes corresponding time data andlocation data.

Example 29 is the subject matter of any of Examples 26-28, wherein thedata received from the one or more AVs is acceleration data detected byan inertial measurement sensor of the one or more AVs.

Example 30 is the subject matter of any of Examples 26-29, wherein thedata received from the one or more AVs comprises state estimationinformation and acceleration information.

Example 31 is the subject matter of Examples 30, further comprisingstandardizing the acceleration information to generate standardizedacceleration information, wherein the state estimation information andthe standardized acceleration information are aggregated to generate theaggregated data.

Example 32 is the subject matter of Example 31, wherein standardizingthe acceleration information includes performing a linear transform onthe acceleration information to generate the standardized accelerationinformation.

Example 33 is the subject matter of any of Examples 26-32, whereingenerating the vector-field map includes performing a mean filteringand/or a probabilistic filtering on the aggregated data to generate thevector-field map.

Example 34 is the subject matter of any of Examples 26-33, wherein theone or more predetermined threshold values comprise a magnitudethreshold value and/or a frequency threshold value.

Example 35 is the subject matter of any of Examples 26-34, wherein theanalyzing the vector-field map comprises analyzing a magnitude and/or afrequency of vectors of the vector-field map to identify the one or morevectors that exceeding a predetermined magnitude threshold value and/orfrequency threshold value.

Example 36 is a non-transitory computer-readable storage medium with anexecutable computer program stored thereon, the program instructing aprocessor to perform the operations of any of Examples 26-35.

Example 37 is a computer program product having a computer program whichis directly loadable into a memory of a controller, when executed by thecontroller, causes the controller to perform the operations of any ofExamples 26-35.

Example 38 is an apparatus as shown and described.

Example 39 is a method as shown and described.

Example 40 is a non-transitory computer-readable storage medium with anexecutable computer program stored thereon, the program instructing aprocessor to perform the method of Example 39.

Example 41 is a computer program product having a computer program whichis directly loadable into a memory of a controller, when executed by thecontroller, causes the controller to perform the method of Example 39.

CONCLUSION

The aforementioned description of the specific aspects will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific aspects, without undueexperimentation, and without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed aspects, based on the teaching and guidance presented herein.It is to be understood that the phraseology or terminology herein is forthe purpose of description and not of limitation, such that theterminology or phraseology of the present specification is to beinterpreted by the skilled artisan in light of the teachings andguidance.

References in the specification to “one aspect,” “an aspect,” “anexemplary aspect,” etc., indicate that the aspect described may includea particular feature, structure, or characteristic, but every aspect maynot necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same aspect. Further, when a particular feature, structure, orcharacteristic is described in connection with an aspect, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother aspects whether or not explicitly described.

The exemplary aspects described herein are provided for illustrativepurposes, and are not limiting. Other exemplary aspects are possible,and modifications may be made to the exemplary aspects. Therefore, thespecification is not meant to limit the disclosure. Rather, the scope ofthe disclosure is defined only in accordance with the following claimsand their equivalents.

Aspects may be implemented in hardware (e.g., circuits), firmware,software, or any combination thereof. Aspects may also be implemented asinstructions stored on a machine-readable medium, which may be read andexecuted by one or more processors. A machine-readable medium mayinclude any mechanism for storing or transmitting information in a formreadable by a machine (e.g., a computing device). For example, amachine-readable medium may include read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; electrical, optical, acoustical or other forms ofpropagated signals (e.g., carrier waves, infrared signals, digitalsignals, etc.), and others. Further, firmware, software, routines,instructions may be described herein as performing certain actions.However, it should be appreciated that such descriptions are merely forconvenience and that such actions in fact results from computingdevices, processors, controllers, or other devices executing thefirmware, software, routines, instructions, etc. Further, any of theimplementation variations may be carried out by a general purposecomputer.

For the purposes of this discussion, the term “processing circuitry” or“processor circuitry” shall be understood to be circuit(s),processor(s), logic, or a combination thereof. For example, a circuitcan include an analog circuit, a digital circuit, programmableprocessing circuit, state machine logic, other structural electronichardware, or a combination thereof. A processor can include amicroprocessor, a digital signal processor (DSP), or other hardwareprocessor. The processor can be “hard-coded” with instructions toperform corresponding function(s) according to aspects described herein.Alternatively, the processor can access an internal and/or externalmemory to retrieve instructions stored in the memory, which whenexecuted by the processor, perform the corresponding function(s)associated with the processor, and/or one or more functions and/oroperations related to the operation of a component having the processorincluded therein.

In one or more of the exemplary aspects described herein, processingcircuitry can include memory that stores data and/or instructions. Thememory can be any well-known volatile and/or non-volatile memory,including, for example, read-only memory (ROM), random access memory(RAM), flash memory, a magnetic storage media, an optical disc, erasableprogrammable read only memory (EPROM), and programmable read only memory(PROM). The memory can be non-removable, removable, or a combination ofboth.

Various aspects herein may utilize one or more machine learning modelsto perform corresponding functions of the MGS 205, the AVs 202, an AVstack, and/or one or more other functions described herein. A machinelearning model may be executed by a computing system to progressivelyimprove performance of a specific task. In some aspects, parameters of amachine learning model may be adjusted during a training phase based ontraining data. A trained machine learning model may then be used duringan inference phase to make predictions or decisions based on input data.

The machine learning models described herein may take any suitable formor utilize any suitable techniques. For example, any of the machinelearning models may utilize supervised learning, semi-supervisedlearning, unsupervised learning, or reinforcement learning techniques.

In supervised learning, the model may be built using a training set ofdata that contains both the inputs and corresponding desired outputs.Each training instance may include one or more inputs and a desiredoutput. Training may include iterating through training instances andusing an objective function to teach the model to predict the output fornew inputs. In semi-supervised learning, a portion of the inputs in thetraining set may be missing the desired outputs.

In unsupervised learning, the model may be built from a set of datawhich contains only inputs and no desired outputs. The unsupervisedmodel may be used to find structure in the data (e.g., grouping orclustering of data points) by discovering patterns in the data.Techniques that may be implemented in an unsupervised learning modelinclude, e.g., self-organizing maps, nearest-neighbor mapping, k-meansclustering, and singular value decomposition.

Reinforcement learning models may be given positive or negative feedbackto improve accuracy. A reinforcement learning model may attempt tomaximize one or more objectives/rewards. Techniques that may beimplemented in a reinforcement learning model may include, e.g.,Q-learning, temporal difference (TD), and deep adversarial networks.

Various aspects described herein may utilize one or more classificationmodels. In a classification model, the outputs may be restricted to alimited set of values. The classification model may output a class foran input set of one or more input values. References herein toclassification models may contemplate a model that implements, e.g., anyone or more of the following techniques: linear classifiers (e.g.,logistic regression or naïve Bayes classifier), support vector machines,decision trees, boosted trees, random forest, neural networks, ornearest neighbor.

Various aspects described herein may utilize one or more regressionmodels. A regression model may output a numerical value from acontinuous range based on an input set of one or more values. Referencesherein to regression models may contemplate a model that implements,e.g., any one or more of the following techniques (or other suitabletechniques): linear regression, decision trees, random forest, or neuralnetworks.

What is claimed is:
 1. A map generation system, comprising: atransceiver configured to receive historical data from one or moreautonomous vehicles (AVs), wherein the historical data includes stateestimation information and acceleration information; a controllerconfigured to: aggregate the historical data received from the one ormore AVs, to index one or more acceleration values of the accelerationinformation to corresponding one or more state values of the stateestimation information, to generate aggregated data; generate avector-field map segmented into a plurality of location-based cellsbased on the aggregated data, the generation of the vector-field mapincluding: mapping the one or more acceleration values of theacceleration information to each of the plurality of location-basedcells based on the respective one or more state values of the stateestimation information; and processing the mapped one or moreacceleration values to generate respective acceleration vectors for theplurality of location-based cells; analyze the vector-field map toidentify one or more of the acceleration vectors exceeding one or morepredetermined threshold values to generate analysis informationcorresponding to the identified one or more vectors; and transmit theanalysis information to the one or more AVs using the transceiver,wherein at least one of the one or more AVs adapt its operation based onthe received analysis information.
 2. The map generation system of claim1, wherein the state estimation information includes corresponding timedata and location data.
 3. The map generation system of claim 1, whereinthe acceleration information includes acceleration data detected by aninertial measurement sensor of the one or more AVs.
 4. The mapgeneration system of claim 1, wherein the controller comprises: astandardization processor that is configured to standardize theacceleration information to generate standardized accelerationinformation, and a data aggregator that is configured to aggregate thestate estimation information and the standardized accelerationinformation to generate the aggregated data.
 5. The map generationsystem of claim 4, wherein the controller is configured to separate thereceived historical data into the acceleration information and the stateestimation information to separately provide the accelerationinformation to the standardization processor and the state estimationinformation to the data aggregator.
 6. The map generation system ofclaim 1, wherein the controller is configured to perform a meanfiltering and/or a probabilistic filtering on the aggregated data togenerate the vectors of the respective cells of the vector-field map. 7.The map generation system of claim 1, wherein the one or morepredetermined threshold values comprise a magnitude threshold valueand/or a frequency threshold value.
 8. The map generation system ofclaim 1, wherein the controller is configured to analyze a magnitudeand/or a frequency of the vectors of the vector-field map to identifythe one or more vectors that exceed a predetermined magnitude thresholdvalue and/or frequency threshold value.
 9. The map generation system ofclaim 1, wherein each of the plurality of location-based cells comprisesa single acceleration vector.
 10. The map generation system of claim 1,wherein the plurality of location-based cells are arranged in a grid toform the vector-field map.
 11. The map generation system of claim 1,wherein the controller is configured to perform a first filteringoperation and a second filtering operation on the mapped one or moreacceleration values to generate respective acceleration vectors for theplurality of location-based cells, wherein the second filteringoperation is slower but more accurate than the first filteringoperation.
 12. The map generation system of claim 1, wherein thecontroller is configured to: determine a rate-of-change of theacceleration vectors within a cell of the plurality of location-basedcells; and determine a magnitude of the acceleration vectors within acell of the plurality of location-based cells, the one or more of theacceleration vectors being identified based on the determinedrate-of-change of the acceleration vectors and the magnitude of theacceleration vectors.
 13. A non-transitory computer-readable storagemedium with an executable computer program stored thereon, the programinstructing a processor to: receive historical data from one or moreautonomous vehicles (AVs) using a transceiver, wherein the historicaldata includes state estimation information and acceleration information;aggregate the historical data received from the one or more AVs, toindex one or more acceleration values of the acceleration information tocorresponding one or more state values of the state estimationinformation, to generate aggregated data; generate a vector-field mapsegmented into a plurality of location-based cells based on theaggregated data, the generation of the vector-field map including:mapping the one or more acceleration values of the accelerationinformation to each of the plurality of location-based cells based onthe respective one or more state values of the state estimationinformation; and processing the mapped one or more acceleration valuesto generate respective acceleration vectors for the plurality oflocation-based cells; analyze the vector-field map to identify one ormore of the acceleration vectors exceeding one or more predeterminedthreshold values to generate analysis information corresponding to theidentified one or more vectors; and transmit the analysis information tothe one or more AVs using the transceiver, wherein at least one of theone or more AVs adapt its operation based on the received analysisinformation.
 14. The non-transitory computer-readable storage medium ofclaim 13, wherein the state estimation information includescorresponding time data and location data.
 15. The non-transitorycomputer-readable storage medium of claim 13, wherein the accelerationinformation includes acceleration data detected by an inertialmeasurement sensor of the one or more AVs.
 16. The non-transitorycomputer-readable storage medium of claim 13, wherein the programfurther instructs the processor to: standardize the accelerationinformation to generate standardized acceleration information, aggregatethe state estimation information and the standardized accelerationinformation to generate the aggregated data.
 17. The non-transitorycomputer-readable storage medium of claim 16, wherein standardizing theacceleration information includes performing a linear transform on theacceleration information to generate the standardized accelerationinformation.
 18. The non-transitory computer-readable storage medium ofclaim 13, wherein generating the vector-field map includes performing amean filtering and/or a probabilistic filtering on the aggregated datato generate the vectors of the respective cells of the vector-field map.19. The non-transitory computer-readable storage medium of claim 13,wherein the one or more predetermined threshold values comprise amagnitude threshold value and/or a frequency threshold value.
 20. Thenon-transitory computer-readable storage medium of claim 13, wherein theanalyzing the vector-field map comprises analyzing a magnitude and/or afrequency of the vectors of the vector-field map to identify the one ormore vectors that exceed a predetermined magnitude threshold valueand/or frequency threshold value.
 21. An autonomous vehicle (AV),comprising: an inertial measurement sensor configured to detectacceleration data of the AV; a controller configured to index thedetected acceleration data to state estimation information includingrespective locations at which the acceleration data was detected; and atransceiver that is configured to: transmit the detected accelerationdata and corresponding state estimation information to a map generationsystem; and receive, from the map generation system, a vector-field mapsegmented into a plurality of location-based cells, the vector-field mapbeing generated based on the detected acceleration data and the stateestimation information, the acceleration data being mapped to a cell ofthe location-based cells associated with a location corresponding to thestate estimation information, wherein each of the cells includes anacceleration vector determined based on the acceleration data and thestate estimation information, wherein the controller is furtherconfigured to control the AV based on the vector-field map.
 22. The AVof claim 21, wherein the state estimation data further includescorresponding time data.
 23. An autonomous vehicle system, comprising:one or more autonomous vehicles (AVs); and a map generation system thatincludes a controller configured to: aggregate historical data receivedfrom the one or more AVs to generate aggregated data, the historicaldata including state estimation information and accelerationinformation, wherein the aggregation of the historical data includesindexing one or more acceleration values of the acceleration informationto corresponding one or more state values of the state estimationinformation; generate a vector-field map segmented into a plurality oflocation-based cells based on the aggregated data, the generation of thevector-field map including: mapping the one or more acceleration valuesof the acceleration information to each of the plurality oflocation-based cells based on the respective one or more state values ofthe state estimation information; and processing the mapped one or moreacceleration values to generate respective acceleration vectors for theplurality of location-based cells; analyze the vector-field map toidentify one or more of the acceleration vectors exceeding one or morepredetermined threshold values; and generate analysis informationcorresponding to the identified one or more vectors and provide theanalysis information to the one or more AVs.
 24. The autonomous vehiclesystem of claim 23, wherein the one or more AVs each include an inertialmeasurement sensor configured to detect acceleration data, wherein thehistorical data received from the one or more AVs is the accelerationdata detected by the inertial measurement sensor.